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Title: Aggregate modeling of fast-acting demand response and control under real-time pricing

Abstract

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop a more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. Finally, the results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existingmore » load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.« less

Authors:
 [1];  [2]
  1. Univ. of Victoria, Victoria, BC (Canada); SLAC National Accelerator Lab., Menlo Park, CA (United States)
  2. Univ. of Victoria, Victoria, BC (Canada)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1347548
Grant/Contract Number:
AC02-76SF00515
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 181; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; electricity demand response; demand elasticity; real-time pricing; indirect load control; transactive systems; random utility model

Citation Formats

Chassin, David P., and Rondeau, Daniel. Aggregate modeling of fast-acting demand response and control under real-time pricing. United States: N. p., 2016. Web. doi:10.1016/j.apenergy.2016.08.071.
Chassin, David P., & Rondeau, Daniel. Aggregate modeling of fast-acting demand response and control under real-time pricing. United States. doi:10.1016/j.apenergy.2016.08.071.
Chassin, David P., and Rondeau, Daniel. 2016. "Aggregate modeling of fast-acting demand response and control under real-time pricing". United States. doi:10.1016/j.apenergy.2016.08.071. https://www.osti.gov/servlets/purl/1347548.
@article{osti_1347548,
title = {Aggregate modeling of fast-acting demand response and control under real-time pricing},
author = {Chassin, David P. and Rondeau, Daniel},
abstractNote = {This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop a more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. Finally, the results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.},
doi = {10.1016/j.apenergy.2016.08.071},
journal = {Applied Energy},
number = C,
volume = 181,
place = {United States},
year = 2016,
month = 8
}

Journal Article:
Free Publicly Available Full Text
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Cited by: 2works
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  • This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop amore » more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. The results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.« less
  • Coordinated operation of distributed thermostatic loads such as heat pumps and air conditioners can reduce energy costs and prevents grid congestion, while maintaining room temperatures in the comfort range set by consumers. This paper furthers efforts towards enabling thermostatically controlled loads (TCLs) to participate in real-time retail electricity markets under a transactive control paradigm. An agent-based approach is used to develop an effective and low complexity demand response control scheme for TCLs. The proposed scheme adjusts aggregated thermostatic loads according to real-time grid conditions under both heating and cooling modes. Here, a case study is presented showing the method reducesmore » consumer electricity costs by over 10% compared to uncoordinated operation.« less
  • Demand-side management (DSM) programs in the industrial sector appear to be economically feasible due to the large controllable loads and relatively low costs per control point. Innovative electricity tariffs provide one of the most important DSM alternatives. Because real-time pricing (RTP) is considered as an excellent management option which reflects the real cost of generating electricity to the end user, the electricity cost saving potential of RTP through demand management is presented in this paper. A unique analytical approach is followed to describe the potential electricity cost savings mathematically in terms of variables familiar to both the end user andmore » utility. These variables include the installed power consumption capacity of the plant, the plant`s spare energy consumption capacity, and terms that describe the structure of the RTP tariff.« less